From f11b4b9deed8419165a3bb9b7406a00017ad7a66 Mon Sep 17 00:00:00 2001 From: papayiv <55081543+papayiv@users.noreply.github.com> Date: Tue, 3 Mar 2026 17:57:39 +0300 Subject: [PATCH 1/4] Create PLAN.md --- PLAN.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 PLAN.md diff --git a/PLAN.md b/PLAN.md new file mode 100644 index 0000000..6165149 --- /dev/null +++ b/PLAN.md @@ -0,0 +1,14 @@ +# DataMetaMap Backlog +## Goal +DataMetaMap compares datasets in a unified vector space to find similarities. It operates on the principle that if a model performs well on one dataset, it will also perform well on semantically similar (nearby in embedding space) data. + +## Development Steps: + - Research & Method Study – Review existing approaches for dataset's embedding, similarity measurement, and transferability estimation. 3 + - Data Collection – Gather a diverse set of datasets for experimentation and benchmarking. + - Method Implementation – Implement core algorithms to embed datasets into a shared vector space and compute similarities. + - Test Coverage – Develop unit and integration tests to ensure reliability. + - Benchmark & Visualization – Run benchmarks and create visualizations (e.g., similarity matrices). + - Technical Report – Document methodology, experiments, and results. + - Documentation – Write comprehensive user and contributor documentation. + - Blog Post – Prepare an explanatory blog post highlighting the project's value. + - Demo Code – Provide example notebooks/scripts demonstrating real-world usage. From 811df72706366874175d634680320a8e86aea78a Mon Sep 17 00:00:00 2001 From: Ilia <95166972+ILIAHHne63@users.noreply.github.com> Date: Tue, 3 Mar 2026 18:05:42 +0300 Subject: [PATCH 2/4] Update PLAN.md --- PLAN.md | 56 ++++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 42 insertions(+), 14 deletions(-) diff --git a/PLAN.md b/PLAN.md index 6165149..06bfb78 100644 --- a/PLAN.md +++ b/PLAN.md @@ -1,14 +1,42 @@ -# DataMetaMap Backlog -## Goal -DataMetaMap compares datasets in a unified vector space to find similarities. It operates on the principle that if a model performs well on one dataset, it will also perform well on semantically similar (nearby in embedding space) data. - -## Development Steps: - - Research & Method Study – Review existing approaches for dataset's embedding, similarity measurement, and transferability estimation. 3 - - Data Collection – Gather a diverse set of datasets for experimentation and benchmarking. - - Method Implementation – Implement core algorithms to embed datasets into a shared vector space and compute similarities. - - Test Coverage – Develop unit and integration tests to ensure reliability. - - Benchmark & Visualization – Run benchmarks and create visualizations (e.g., similarity matrices). - - Technical Report – Document methodology, experiments, and results. - - Documentation – Write comprehensive user and contributor documentation. - - Blog Post – Prepare an explanatory blog post highlighting the project's value. - - Demo Code – Provide example notebooks/scripts demonstrating real-world usage. +# DataMetaMap Project Plan + +## Project Goal +DataMetaMap aims to compare datasets within a unified vector space to identify semantic similarities. The core idea is that if a model performs well on one dataset, it will likely perform well on semantically similar datasets nearby in embedding space. + +--- + +## Development Phases & Tasks + +### Phase 1: Research and Preparation +- **Literature Review** + Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices. + +- **Data Collection** + Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats. + +- **Planning and Specifications** + Define technical specifications and success criteria based on research findings and data availability. + +--- + +### Phase 2: Implementation and Testing +- **Core Algorithm Development** + Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them. + +- **Testing and Quality Assurance** + Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods. + +- **Benchmarking and Visualization** + Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results. + +--- + +### Phase 3: Documentation and Dissemination +- **Technical Report** + Document the methodology, experimental setup, and findings in a comprehensive technical report. + +- **User and Developer Documentation** + Create detailed documentation for users and contributors, including setup guides and API references. + +- **Demo Examples and Blog Post** + Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights. From 62e40783e6251d08a29192e7231efb7f94c4d385 Mon Sep 17 00:00:00 2001 From: papayiv <55081543+papayiv@users.noreply.github.com> Date: Tue, 3 Mar 2026 21:34:23 +0300 Subject: [PATCH 3/4] Update PLAN.md --- PLAN.md | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) diff --git a/PLAN.md b/PLAN.md index 06bfb78..8e43370 100644 --- a/PLAN.md +++ b/PLAN.md @@ -40,3 +40,63 @@ DataMetaMap aims to compare datasets within a unified vector space to identify s - **Demo Examples and Blog Post** Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights. + +## Remastered + +### Phase 1: Research and Preparation +- **Literature Review** + Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices. + +- **Baseline Selection** + Identify and select baseline methods from literature for comparison during benchmarking. + +- **Data Collection** + Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats. + +- **Data Preprocessing Pipeline** + Design and implement preprocessing steps to handle different dataset formats and ensure consistent input for embedding methods. + +- **Evaluation Metrics Definition** + Define quantitative metrics to evaluate embedding quality and similarity measurement accuracy. + +- **Planning and Specifications** + Define technical specifications and success criteria based on research findings and data availability. + +--- + +### Phase 2: Implementation and Testing +- **Core Algorithm Development** + Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them. + +- **Baseline Implementations** + Implement selected baseline methods from literature for comparison. + +- **Testing and Quality Assurance** + Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods. + +- **Performance Optimization** + Profile and optimize code for memory efficiency and computational speed, especially for large datasets. + +- **Error Handling and Logging** + Implement robust error handling and logging mechanisms for debugging and monitoring. + +- **Benchmarking and Visualization** + Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results. + +--- + +### Phase 3: Documentation and Dissemination +- **Technical Report** + Document the methodology, experimental setup, and findings in a comprehensive technical report. + +- **User and Developer Documentation** + Create detailed documentation for users and contributors, including setup guides and API references. + +- **Demo Examples and Blog Post** + Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights. + +- **Benchmark Results Repository** + Publish benchmark results, precomputed embeddings, and similarity matrices in a public repository for reproducibility. + +- **Future Work Roadmap** + Outline potential extensions, improvements, and research directions based on current findings. From 189ebf5b2924a6d1e74a9808c69d91343b862830 Mon Sep 17 00:00:00 2001 From: papayiv <55081543+papayiv@users.noreply.github.com> Date: Tue, 3 Mar 2026 21:37:47 +0300 Subject: [PATCH 4/4] Update PLAN.md --- PLAN.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PLAN.md b/PLAN.md index 8e43370..38933c3 100644 --- a/PLAN.md +++ b/PLAN.md @@ -90,7 +90,7 @@ DataMetaMap aims to compare datasets within a unified vector space to identify s Document the methodology, experimental setup, and findings in a comprehensive technical report. - **User and Developer Documentation** - Create detailed documentation for users and contributors, including setup guides and API references. + Create detailed documentation for users and contributors, including setup guides and API references. In this task we should create github.io page where user can find documentation for all classes and their methods. Github.io page must have headers for functions and links to their each source code. - **Demo Examples and Blog Post** Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.